Digital Signal Processing Reference
In-Depth Information
techniques such as ACO and PSO to solve this problem. Swarm filters can be con-
sidered as a subset of a more general class of filters, called heuristic filters, where
the heuristic optimization algorithms are utilized to dynamically solve the state es-
timation problem. Although many heuristic optimization algorithms have been de-
veloped by now, the field of heuristic filtering is still in its first days of development
and a huge amount of work is left to be performed.
References
1. Siouris, G.M.: An Engineering Approach to Optimal Control and Estimation Theory. Air
Force Institute of Technology, New York (1995)
2. Brayson, A.E., Ho, Y.C.: Applied Optimal Control. Blaisdell Publishing Company, Waltham
(1969)
3. Ristic, B., Arulampalam, S., Gordon, N.: Beyond the Kalman Filter: Particle Filters for Track-
ing Applications. Blaisdell Publishing Company, Artech House, London (2004)
4. Kalman, R.E.: A new approach to linear filtering and prediction problems. Trans. ASME J.
Basic Eng. 82 (Series D), 35-45 (1960)
5. Jazwinski, A.H.: Stochastic Processes and Filtering Theory. Academic Press, New York
(1970)
6. Julier, S.J., Uhlmann, J.K.: A new extension of the Kalman filter to nonlinear systems. In:
AeroSense 11th International Symposium Aerospace Defense Sensing, Simulation and Con-
trols, pp. 182-193 (1960)
7. Carpentier, J., Clifford, P., Fernhead, P.: An improved particle filter for non-linear problems.
IEE Proc. Radar Sonar Navig. 146 (1), 2-7 (1999)
8. Nobahari, H., Sharifi, A.: A novel heuristic filter based on ant colony optimization for non-
linear systems state estimation. In: Computational Intelligence and Intelligent Systems, 6th
International Symposium, CCIS, Wuhan, China, vol. 316, pp. 20-29 (2012)
9. Arulampalam, M.S., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for online
nonlinear/non-Gaussian Bayesian tracking. J. Stat. Comput. Simul. 50 (1), 1-23 (1997)
10. Higuchi, T.: Monte Carlo filter using the genetic algorithm operators. J. Stat. Comput. Simul.
59 (1), 1-23 (1997)
11. Park, S., Hwang, J.P., Kim, E., Kang, H.: A new evolutionary particle filter for the prevention
of sample impoverishment. IEEE Trans. Evol. Comput. 13 (4), 801-809 (2009)
12. Clapp, T.: Statistical Methods for the Processing of Communication Data. University of Cam-
bridge, Cambridge (2000)
13. Troma, P., Szepesvári, C.: LS-N-IPS: an improvement of particle filters by means of local
search. In: Proc. Non-Linear Control Systems (NOLCOS 2001), St. Petersburg, Russia (2001)
14. Tong, G., Fang, Z., Xu, X.: A particle swarm optimized particle filter for nonlinear system
state estimation. In: IEEE Congress on Evolutionary Computation, pp. 438-442 (2006)
15. Zhong, J.P., Fung, Y.F., Dai, M.: A biologically inspired improvement strategy for particle
filter: ant colony optimization assisted particle filter. Int. J. Control. Autom. Syst. 8 (3), 519-
526 (2010)
16. Hao, Z., Zhang, X., Yu, P., Li, H.: Video object tracing based on particle filter with ant colony
optimization. In: 2nd IEEE International Conference, Advance Computer Control, Automa-
tion and Systems, vol. 3, pp. 232-236 (2010)
17. Yu, Y., Zheng, X.: Particle filter with ant colony optimization for frequency offset estimation
in OFDM systems with unknown noise distribution. J. Signal Process. 91 , 1339-1342 (2011)
18. Doucet, A., Godsill, S., Andrieu, C.: On sequential Monte Carlo sampling methods for
Bayesian filtering. Stat. Comput. 10 , 197-208 (2000)
19. Cappe, O., Godsill, S.J., Moulines, E.: An overview of existing methods and recent advances
in sequential Monte Carlo. Proc. IEEE 95 (5), 899-924 (2007)
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